Image enhancement via calibrated lens simulation

ABSTRACT

The description relates to enhancing images. One device includes a lens configured to focus images on an image sensor. The device also includes point spread function (PSF) lens data relating to manufacturing specifications of the lens and a PSF measurement of the lens associated with a test image of a planar calibration pattern at a single depth of field.

BACKGROUND

Image capturing devices, such as cameras, tend to have a lens thatfocuses an image on an image sensor, such as film or a charge coupleddevice (CCD). Lens distortions, called aberrations, limit the sharpnessof the image formed by the lens. Aberrations can be grouped into twogeneral types: those that are caused by design compromises of a lensmodel or specification; and those that are caused by deviations from thelens model when individual lenses are made. These aberrations candecrease image quality even if the lens is perfectly focused.

SUMMARY

The described implementations relate to enhancing images. Oneimplementation can include a lens configured to focus an image on animage sensor that is configured to capture the image. In this case, thelens is a known model. This implementation can also include calibrationdata relating to a planar calibration pattern captured through the lensat a single depth of field. The implementation can include an imageenhancement component configured to receive the captured image and toutilize a point spread function (PSF) calculated from the calibrationdata to produce an enhanced image.

Another implementation can include a lens configured to focus images onan image sensor. The implementation can also include PSF lens datarelating to manufacturing specifications of the lens and a PSFmeasurement of the lens associated with a test image of a planarcalibration pattern at a single depth of field.

The above listed examples are intended to provide a quick reference toaid the reader and are not intended to define the scope of the conceptsdescribed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate implementations of the conceptsconveyed in the present document. Features of the illustratedimplementations can be more readily understood by reference to thefollowing description taken in conjunction with the accompanyingdrawings. Like reference numbers in the various drawings are usedwherever feasible to indicate like elements. Further, the left-mostnumeral of each reference number conveys the Figure and associateddiscussion where the reference number is first introduced.

FIGS. 1-2 are flowcharts of exemplary image enhancement techniques inaccordance with some implementations of the present concepts.

FIGS. 3-4 show exemplary image enhancement systems in accordance withsome implementations of the present concepts.

DETAILED DESCRIPTION Overview

This patent relates to enhancing images (e.g., photos). The images canbe captured by an imaging device, such as a dedicated camera or videocamera, or by a device that offers a camera functionality, such as acell phone, smart phone, or personal digital assistant, among others. Asused herein, a device that offers a camera functionality can be thoughtof as a camera regardless of other functionalities offered by thedevice. The camera can include a lens (or multiple lenses). The lens canbe manufactured according to a lens model specification. The lens modelspecification may knowingly include aberrations in an attempt to lowermanufacturing costs and/or for other reasons. Also, the manufacturingprocess can introduce deviations (e.g., variance) from the modelspecification in individual lenses. These deviations can introduceadditional aberrations. The aberrations can be reduced by deconvolvingan image using a lens point spread function (PSF). However, fullymeasuring the PSF tends to be laborious and prohibitive. Alternatively,the PSF can be simulated if the lens model is known. However, this onlyaddresses aberrations introduced in the lens model and not aberrationsintroduced by manufacturing deviation. As a result, the simulated PSFmay be a poor match to measured data. The present concepts can addressboth types of aberrations. Specifically, the present techniques can takea PSF measurement at a single depth for an individual lens. The PSFmeasurement can then be used to calibrate the lens model. The fittedmodel can then be used to compute the PSF for any desired setting oflens parameters for any scene depth, without additional measurements orcalibration. The fitted model gives deconvolution results comparable tofull measurement but is much more compact and can be accomplished withhundreds of times fewer calibration images.

For purposes of explanation consider introductory FIG. 1, which shows animage enhancement method 100.

This implementation can obtain a specification for a model of a lens(lens model) at 102. The specification can relate to the geometry of thelens. Due to various design considerations, such as cost ofmanufacturing, the lens specification can include parameters that arenot optically ideal. These parameters can cause aberrations in imagescaptured through a lens that conforms to the specifications.

An individual lens model may be utilized in a single model of cameras.For instance, the lens model may be designed for a specific smart phonemodel. In another case, a lens model may be incorporated into multipledifferent camera models. In a further example, the lens model may beinterchangeable between different camera models. For instance, SLR(single-lens reflex) camera lenses may be used with multiple camerasand/or camera models.

A prescription for the lens model can be obtained at 104. In some cases,the lens prescription may be exactly the same as the specification. Inother cases, the prescription may include some parameters from thespecification and ignore other parameters from the specification. Theprescription can be thought of as addressing the aberrations of the lensmodel caused by the design considerations.

For an individual lens of the lens model, the method can capture a testimage at a specific depth of field at 106. In one case, the test imageis a planar point light source array. In one example, the test image canbe captured as part of the lens manufacturing process and/or as part ofthe process of incorporating the lens into a camera. In another example,the test image can be captured by a consumer who purchases the lens.

The method can compute a point spread function (PSF) of the test imagefor the lens at 108. The PSF can be relevant for the specific depth offield of the test image, but less relevant for other depths of field.The PSF of the test image can be utilized to calibrate the lens modelprescription at 110.

The method can obtain an image captured by the lens at a different depthof field than the test image at 112. The calibrated prescription can beutilized to calculate a lens-specific PSF for specific lens parametervalues for focus depths and illumination spectra at 114. Thus, thecalibrated prescription can allow PSFs to be generated that compensatefor parameter value differences between the test image and the capturedimage.

The calculated PSF can be utilized to enhance the image by reducing lensinduced aberrations at 116. The lens induced aberrations can includeaberrations caused by the lens model specification and by deviationsfrom the lens model specification during manufacture of the individuallens.

To summarize, lens aberrations limit the quality of images formed bylenses. These aberrations come from two major sources: the specificationof a lens model and deviations from the lens model imparted on anindividual lens during the manufacturing process. The deviations affectoptical image formation and vary as a function of lens aperture, focallength, light wavelength (chromatic aberration), and/or focusingdistance, among others. Image deconvolution can be used to remove manyaberrations. However, enhanced results can be obtained if the lens PSFis known, allowing the use of non-blind deconvolution. If the PSF is notknown, the PSF can be estimated. Recovering both the PSF and deblurredimage from a single image input (blind deconvolution) is ill-posed andas a result can be unreliable, as the observed blurred image providesonly a partial constraint on the solution.

Unfortunately, accurate PSF measurement is challenging. For a fixedfocal length lens, the PSF can vary with light wavelength, lensaperture, image plane position, object plane distance, and back focalplane distance, among others. Sampling this six dimensional spaceaccurately can entail an inordinate number of images, and can alsoentail precise automated equipment to carry out, especially for morethan one or two lenses. Additionally, PSF values between measured valuestend to be unknown. Existing methods interpolate the measured PSF or fitthe PSF to a parametric model, but these do not generate physicallyaccurate PSFs for the complex lenses that are commonly used inphotography. Some technologies can employ a calibration target bututilize hundreds of test images to obtain accurate PSFs.

The present implementations can simulate the lens PSF using the lensspecification. The lens PSF can be computed with wave optics, such asvia the Rayleigh-Sommerfield diffraction integral. A prescription forthe lens model based upon the lens specification can serve as a startingpoint for an image enhancement process. The image enhancement processcan change lens parameters so that it better matches measured data. Thisfitting process addresses the variations of individual lenses from thelens specification. These variations can cause dramatic PSF variations.

In some implementations, the calibration process can use a small number,even a single photograph, of an in-focus, planar point light sourcearray at a measured depth. After enhancing/optimizing the lens model, aPSF can be generated to be used with image deconvolution to enhanceimage quality.

PSF Simulation

Existing techniques tend to make simplifying assumptions about the lenspoint spread function to improve images. However, real lenses violateall these assumptions. For instance, PSFs of a lens obtained at twoimage positions, two focus/defocus distances, and two focal plane depthsshow significant differences between the PSFs. Even on the optical axis,there are significant differences between the PSF; off-axis thedifferences can be dramatic. Perhaps most surprisingly, the PSF tends tobe strongly dependent on the distance at which the lens is focused.

In the general case, the PSF of a fixed focal length lens can becharacterized as a six dimensional function of the light wavelength (λ),image plane coordinates (x,y), lens aperture (a), lens to objectdistance (d_(obj)), and back focal distance (d_(bf)). PSFs for aparticular lens can be obtained by taking measurements of the lensresponse over these six dimensions, but such methods tend to only beaccurate in limited working volumes and can require a vast amount ofdata to be collected.

Modern lenses tend to be designed using lens CAD models and areprecisely specified by a set of parametric values called the lensspecification or prescription. The present techniques can utilize asingle photograph to calibrate the lens prescription. The calibrated orfitted lens model can be used to generate PSFs at any desired lensparameter values. Accurate PSFs for other parameter values can becalculated from this fitted lens model.

Lens Prescriptions

The lens prescription can be thought of as describing the opticalproperties of the lens: the size, curvature, index of refraction, andtype of coating of each element, among others. To account for chromaticaberration, a dispersion function can model the variation of the indexof refraction with light wavelength, 2. Commonly used functions arepolynomials in either the Schott

n ² =a ₀ +a ₁λ² +a ₂λ⁻²² +a ₃λ⁻⁴ +a ₄λ⁻⁶ +a ₅λ⁻⁸  (1)

or the Sellmeier 1 form

$\begin{matrix}{{n^{2} - 1} = {\frac{K_{1}\lambda^{2}}{\lambda^{2} - L_{1}} + \frac{K_{2}\lambda^{2}}{\lambda^{2} - L_{2}} + {\frac{K_{3}\lambda^{2}}{\lambda^{2} - L_{3}}.}}} & (2)\end{matrix}$

The present techniques are applied to modeling the effect of thefollowing parameters: 1) geometric properties of each optical surface:diameter, radius of curvature (for aspherical surfaces the parameterscould be the coefficients of the polynomial, or other function, whichdescribes the surface), offset along optical axis, and offsetperpendicular to optical axis, 2) coefficients of the dispersionfunction of each material, 3) index of refraction and thickness of eachantireflection coating material, and 4) lens back focal distance. Ofcourse, other parameters can be modeled.

Hybrid Ray Tracing

Given a lens model and a scene, PSFs can be simulated by a combinationof geometric and wave optics. In some cases this hybrid ray tracing/waveoptics simulation can compute the lens PSF by evaluating the scalarRayleigh-Sommerfield diffraction integral:

$\begin{matrix}{{U\left( p_{1} \right)} = {\frac{1}{j\; \lambda}{\int{\int_{\Sigma}{{U\left( p_{0} \right)}\frac{^{jkr}}{r}\cos \mspace{14mu} \theta {s}}}}}} & (3)\end{matrix}$

where λ is the wavelength,

$k = \frac{2\pi}{\lambda}$

is the wavenumber, U(p₀) is the field intensity at point p₀ on thewavefront, r is the distance from a point on the wavefront p₀, and apoint on the image plane, p₁, and θ is the angle between the normal tothe wavefront and the vector p₁−p₀. This integral gives the fieldintensity, U(p₁), at a point p₀ on the image plane. The double integralis evaluated over the surface of the wavefront, (such as the lenssurface closest to the image sensor), and the image plane itself. Forpolychromatic light Eq. 3 is evaluated for each wavelength, λ, and theresults summed incoherently.

In some implementations, rays are traced through all but the last lenssurface using conventional ray tracing, keeping track of the complexphase as the ray passes through optical elements. At the last lenssurface, the ray is treated as a full phasor quantity. In principal, thephasor evaluation for each ray can cover the entire image plane but thismay be computationally infeasible for high resolution sensors. However,for a lens with reasonably corrected aberrations the PSF rapidly fallsto zero away from the ray image plane intersection. This fact can beexploited or leveraged to compute a conservative upper bound on the sizeof the phasor region. This upper bound can greatly reduce computationwhile still accurately representing the PSF of the lens.

One implementation can start by computing a local origin for the PSF byfinding the centroid of all rays intersecting the image plane. Rayswhich intersect the image plane at a distance greater than kd from thelocal origin can be discarded, where k is an empirically chosenconstant, and d is the diameter of the first maximum of the Airy disk.

$d = \frac{1.22\mspace{14mu} \lambda}{NA}$

The numerical aperture, NA, is given by

NA=n sin(θmax)

where n is the index of refraction of the medium the last ray istraveling through. For lenses simulated in air, the index of refractionis close to 1.

In one implementation, the numerical aperture, NA, is computed bysampling along a radial line along the lens. The radial line passesthrough the optical axis. The maximum angle, θ, can be determined. Themaximum angle can be thought of as the greatest angle at which rays fromthe object point are not occluded by the lens.

Some implementations can strive to achieve the highest possibleresolution of the measured PSFs. One such implementation can use amonochrome sensor with small pixels (2.2 microns), and sequential RGB(red green blue) illumination from a three color LED (light emittingdiode) lamp. Because the PSF is dependent on wavelength thisimplementation simulated the PSF at 18 wavelengths for each colorchannel. The resultant PSFs can be summed incoherently to give the finalPSF for each color channel. This implementation can use sequential colorso that artifacts due to demosaicing would not be confounded with theresults of the image corrections. However, other implementations caneasily be used with Bayer demosaicked images, among others. Note alsothat while LED lamps are used in this implementation, otherimplementations can use lasers for the precise wavelengths that theygenerate. Since PSFs are dependent upon wavelength, accurate PSFs can becalculated for each laser's wavelength.

Geometric Distortion Removal

Some implementations can also reduce or remove geometric distortion. Forinstance, geometric ray tracing using the above mentioned simulator, cancompute the correspondence between object space and image space. Theimage can be warped with this correspondence to remove geometricdistortion.

Lens Prescription Calibration

In some implementations the lens simulation can be notated using thefunction S(l,x), which takes a lens prescription l and light sourcepositions x as input, and outputs the corresponding point spreadfunctions P. Let l* and l_(s) denote the actual and nominal lensspecification, respectively. The object of the lens fitting step is tofind δl*≡l*−l_(s).

Optimization Scheme

Some implementations can be viewed as employing enhancement/optimizationmethods. These optimization methods can reduce and/or minimize the L2norm between the measured and the simulated PSFs by adjusting the lensprescription. Denoting the measured PSFs as P*, the objective functionis:

$\begin{matrix}{{\delta \; l^{*}} = \left. \underset{\delta \; l}{\arg \mspace{14mu} \min}||{{S\left( {{l_{s} + {\delta \; l}},x} \right)} - P^{*}}||{}_{2}. \right.} & (4)\end{matrix}$

Given that δl is very small and S is smooth around l_(s), the firstorder approximation on S(l_(s)+δl,x) is

$\begin{matrix}{{{S\left( {{l_{s} + {\delta \; l}},x} \right)} \approx {{S\left( l_{s} \right)} + {\frac{\partial S}{\partial l}\delta \; l}}},} & (5)\end{matrix}$

where S(l_(s)) is the PSF simulated using the nominal lens prescription,and

$\frac{\partial S}{\partial l}$

is the Jacobian at l_(s), which is denoted by J. Denoting δP=P*−S(l_(s))as the difference between simulated and measured PSFs and combining Eq.4 and Eq. 5 gives

$\begin{matrix}{{\delta \; l^{*}} = {\left. \underset{\delta \; l}{\arg \mspace{14mu} \min}||{{J\; \delta \; l} - {\delta \; P}} \right.||_{2} = {J^{\dagger}\delta \; P}}} & (6)\end{matrix}$

In some implementations, the calibration process first calculates theJacobian, and then applies the Jacobian pseudo-inverse to the differencebetween measured and simulated PSFs. In practice, S is not linear to l,so δl can be multiplied by a damping factor k_(d)<1, and iterate severaltimes until convergence, which typically takes 3 to 5 iterations. Thisoptimization scheme is shown in FIG. 2.

Briefly, FIG. 2 shows a lens prescription calibration method 200. Themethod starts with an initial lens prescription at 202. This initiallens prescription can be thought of as a lens model prescription. Thelens model prescription can be used as the basis of the current lensprescription at 204. Thus, this initial lens prescription may onlyaddress aberrations caused by the lens specification. Ray tracing 206can be performed on the current lens prescription to produce simulatedPSFs at 208. A test image can be captured with the individual lens at210. Measured PSFs 212 can be obtained from the test image. The measuredPSFs 212 and the simulated PSFs 208 can be compared at 214 to contributeto the lens fitting optimization 216. The measured PSFs addressdeviations in the individual lens from the lens model specification.Accordingly, the lens fitting optimization can address both lens modelaberrations and individual lens aberrations. The lens fitting can beutilized to update the current lens prescription 204 as indicated at218. This process can be repeated in an iterative fashion to enhance theaccuracy of the lens prescription.

Optimized Variables

The variables in the optimization scheme are parameters in the lensprescription. Here those parameters are divided into three groups bytheir properties: surface variables, glass variables, and cameravariables. Their effects on the PSF are discussed below.

Surface Variables

This group consists of the radius of curvature, XY offset (perpendicularto the optical axis), and Z offset (parallel to optical axis) of eachoptical element. This implementation operates on the assumption that thelenses are spherical lenses. As such, surface tilting can be modeled bya combination of X, Y, and Z offsets. Other implementations can operateon aspherical lenses. The aspherical lenses can be characterized as aset of curved (or radial) surfaces starting at the center of the lensand extending radially therefrom. The set of curved surfaces can beexpressed as polynomials.

Glass Variables

This group consists of the coefficients of the dispersion functionformula. The dispersion function affects chromatic aberration asdifferent wavelengths have different refraction indices. Chromaticaberration tends to be highly affected by the first derivative of thedispersion function

$\frac{n}{\lambda}.$

As a simplification some implementations only optimize this firstderivative for each glass. The PSF shapes tend to be similar, but thechromatic aberration changes significantly.

Camera Variables.

This group consists of the object distance and back focal length. Smalldifferences in the back focal length can have a significant effect onthe PSF. For example, if the physical focusing process focuses on bluelight and the simulator focuses on red light, then the simulated bluePSF component will be smaller than the actual one. Thus, the actual backfocal length is calibrated instead. Variations in the object distancehave little effect on the simulated PSF, so it may or may not beincluded in the optimization.

The order in which the example methods are described is not intended tobe construed as a limitation, and any number of the described blocks oracts can be combined in any order to implement the methods, or alternatemethods. Furthermore, the methods can be implemented in any suitablehardware, software, firmware, or combination thereof, such that acomputing device can implement the method. In one case, the method isstored on one or more computer-readable storage media as a set ofinstructions such that execution by a processor of a computing devicecauses the computing device to perform the method.

FIG. 3 shows a calibration system 300. This calibration system isexplained relative to two cameras 302(1) and 302(2), for purposes ofexplanation. These cameras each include a lens 304, an image sensor 306and an image enhancement component 312 (with the suffix ‘1’ utilized torefer to camera 302(1) and the suffix ‘2’ utilized to refer to camera302(2)). In this example, camera 302(1) is manifest as a smartphone andcamera 302(2) is manifest as an SLR camera body. The SLR camera body canbe removably coupled to multiple interchangeable ‘lens devices’. Theterm ‘lens device’ 314 as used herein can include the lens 304(2)(and/or multiple lenses) and other components, such as a housing 316.

During manufacture, or at a subsequent time, a test image 320 can becaptured with an individual camera 302(1) and/or 302(2). In the case ofcamera 302(1), the test image 320(1) is captured through a calibrationtarget 322(1). Illumination for the test image 320(1) is provided bythree LEDs 324, 326, and 328 that are configured to provide green, red,and blue wavelength light, respectively. In the case of camera 302(2),the test image 320(2) is captured through a calibration target 322(2).Illumination for the test image 320(2) is provided by four lasers 330,332, 334, and 336 that are configured to provide four distinctwavelengths of visible light, respectively. Other implementations canuse other numbers and/or types of light sources besides the three andfour shown here.

The test images 320(1) and 320(2) can be stored locally on the cameraand/or communicated to a database 340. The database can serve to map thetest image to the individual lens that captured the test image. In theillustrated configuration, database 340 includes a camera model column342, a lens model column 344, a lens serial number column 346, a testimage column 348, a lens model prescription column 350, and a calibratedprescription column 352. Other database implementations may includeadditional and/or different columns, such as camera serial number,surface variables, glass variables, and/or camera variables describedabove, etc.

An individual horizontal row of the database 340 can be dedicated to anindividual lens to be identified, such as by serial number. For instancein this example, horizontal row 354 relates to lens 304(1) andhorizontal row 356 relates to lens 304(2). For sake of brevity, the rowsare populated with hypothetical values. As described above and below,the individual lens' test image can be used to calibrate the lens modelprescription for that lens. The calibrated prescription can then be usedto enhance other images captured with the lens. One such configurationis described below relative to FIG. 4.

In some configurations, an individual camera 302(1) or 302(2) cancompute its corresponding calculated prescription from the test image322(1) or 322(2), respectively. In other configurations, thecomputations may be performed at the database 340 and then thecalculated prescription can be sent to the camera for local storage. Thecamera's image enhancement component 312 can utilize the prescription toenhance subsequent images captured by the camera. In still otherconfigurations, the calculated prescription may be maintained at thedatabase and applied to other images received from the camera to produceenhanced images. These enhanced images can then be accessed and/ordownloaded by a user of the camera or other authorized user. In stillother implementations, the test image may simply be stored until apredefined condition occurs. For instance, the test image can be storeduntil a user of the camera requests image enhancement. At that point,the test image can be processed as described above to generate thecalibrated prescription utilized in the image enhancement process. Sucha configuration can reduce resource usage involved in calculatingcalibrated prescriptions that may never actually be used for variousreasons.

Lens Measurements

Cameras 302(1) and 302(2) can be utilized to capture test images ofcalibration targets 322(1) and 322(2), respectively. For each testimage, the object distance (OD) can be measured and the back focaldistance (BFD), among other parameters, can be estimated. Thepoint-spread function can be simulated with these parameters.

The information from the test images can be used to calibrate andmeasure the simulated point spread functions. A standard I3A/ISOResolution Test Chart from BHPhoto can be used to measure effectiveimage resolution. Impulse responses can be measured with calibrationtargets manifest as specially prepared boards. In one example, theboards can consist of laser-cut 0:1 mm diameter pinholes into analuminized Mylar sheet. The Mylar sheet can then be mounted on a flatacrylic backing coated with a diffusing material. This calibrationtarget can be backlit with the light source.

Calibration

Lens calibration can involve a high number of variables. Non-linearoptimization of this number of variables can be challenging. Instead ofoptimizing all variables together, the variables can be divided intogroups, such as three groups. Individual groups can then be seriallyoptimized. In one implementation, the damping factor, k_(d), is set to0.7, and iterated multiple times.

The calibration process can take multiple measured PSF samples for theindividual respective lenses. These PSFs can be measured at a singlefocusing distance and captured with a single test image. Theun-calibrated PSFs tend to have more significant differences at thecorners, so sampling density can be higher at the edges than at thecenter. While a single photograph and focal plane can be used forcalibration, the extension to multiple planes can be employed.

The manufacturing tolerance of each parameter tends to be on the orderof 1%, so the offsets can be set to 0.5% for radius, 10⁻⁵ m for XYZoffsets, 1% of dispersion at the red frequency for the dispersionfunction offset, and 10⁻⁵ m for back focal length, in oneimplementation. The numerical derivatives can be approximated with a twosided finite difference.

During the simulation, super samples of the PSF up to 3× of the actualcamera resolution can be used to avoid aliasing. Both PSF computationand Jacobian calculation can be performed in parallel.

FIG. 4 shows another system 400 that includes camera 302(1) and acomputer 402. In this implementation, camera 302(1) includes a processor404(1) and storage 406(1). Similarly, computer 402 includes a processor404(2) and storage 406(2). Accordingly, in the present discussion,camera 302(1) can be characterized as a computer.

The term “computer” or “computing device” as used herein can mean anytype of device that has some amount of processing capability and/orstorage capability. Processing capability can be provided by one or moreprocessors that can execute data in the form of computer-readableinstructions to provide a functionality. Data, such as computer-readableinstructions, can be stored on storage 406. The storage can be internaland/or external to the computer. The storage can include any one or moreof volatile or non-volatile memory, hard drives, flash storage devices,and/or optical storage devices (e.g., CDs, DVDs etc.), among others. Asused herein, the term “computer-readable media” can include transitoryand non-transitory instructions. In contrast, the term“computer-readable storage media” excludes transitory instances.Computer-readable storage media can include “computer-readable storagedevices”. Examples of computer-readable storage devices include volatilestorage media, such as RAM, and non-volatile storage media, such as harddrives, optical discs, and flash memory, among others.

Returning to FIG. 4, camera 302(1) can capture an image via lens 304(1).(The lens 304(1) is positioned on a back surface of the camera and istherefore shown in ‘ghost’ (e.g., dashed lines)). In someimplementations, the image enhancement component 312(1) can utilize thecalibrated prescription (cal spec 1) to enhance the captured image tocreate an enhanced image. In this example, the calibrated prescriptioncan be obtained from row 354 of database 340 of FIG. 3. The enhancedimage can be of higher quality in regards to one or multiple parameters.This aspect is described in more detail above and below. In one suchimplementation, assume that the calibrated prescription was determinedfor the camera as described above relative to FIG. 3. and that thecalibrated prescription was installed on non-volatile storage of thecamera as part of the manufacturing process, such as by the manufactureror another entity. In such a configuration, the image enhancementcomponent can be configured to receive the captured image and to utilizea PSF calculated from the calibration data (e.g., the calibratedprescription) to produce the enhanced image.

In another implementation, camera 302(1) can capture an image 410. Thecamera can associate the calibrated prescription “Cal Spec 1” with thecaptured image 410 in the form of metadata 412. The captured image andthe calibrated prescription can be communicated to computer 402 vianetwork 414. Computer 402 can include an image enhancement component416. This image enhancement component 416 can enhance the captured imageutilizing the calibrated prescription ‘Cal Spec 1’ to produce enhancedimage 418. The enhanced image 418 can be stored by computer 402 and/orsent back to camera 302(1).

In still another implementation, camera 302(1) may not include the testimage or the calibrated prescription, but may include a serial number orother unique identifier that can be associated with captured images asmetadata. Computer 402 can receive the captured image and utilize theunique identifier to obtain the test image or the calibratedprescription from database 340 (FIG. 3). If the computer obtains onlythe test image the computer can generate the calibrated prescription. Ineither case, the computer can use the calibrated prescription to enhancethe captured image. Computer 402 may have more available resources, suchas storage, processing cycles, and/or power, than camera 302(1) and assuch may be able to allow the camera's resources to remain available forother uses, rather than being expended enhancing images.

In some configurations, computer 402 may belong to an owner of camera302(1) or computer 402 may be a server computing device controlled by adifferent entity. Further, while a distinct computer 402 is illustratedhere, the provided functionality can be offered in a cloud computingscenario.

In the illustrated implementation camera 302(1) is configured with ageneral purpose processor 404(1) and storage 406(1). In someconfigurations, the camera can include a system on a chip (SOC) typedesign. In such a case, functionality provided by the camera can beintegrated on a single SOC or multiple coupled SOCs. In one suchexample, the camera can include shared resources and dedicatedresources. An interface(s) can facilitate communication between theshared resources and the dedicated resources. As the name implies,dedicated resources can be thought of as including individual portionsthat are dedicated to achieving specific functionalities. For instance,in this example, the dedicated resources can include image enhancementcomponent 312(1).

Shared resources can be storage, processing units, etc. that can be usedby multiple functionalities. In this example, the shared resources caninclude the processor. In one case, image enhancement component 312(1)can be implemented as dedicated resources. In other configurations, thiscomponent can be implemented on the shared resources and/or theprocessor can be implemented on the dedicated resources. In someconfigurations, the image enhancement component 312(1) can be installedduring manufacture of the camera or by an intermediary that prepares thecamera for sale to the end user. In other instances, the end user mayinstall the image enhancement component 312(1), such as in the form of adownloadable application or from a USB thumb drive, among others.

CONCLUSION

Although techniques, methods, devices, systems, etc., pertaining toimage enhancement scenarios are described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claimed methods, devices, systems, etc.

1. A system, comprising: a lens configured to focus an image on an imagesensor that is configured to capture the image, wherein the lens is aknown model; calibration data relating to a planar calibration patterncaptured through the lens at a single depth of field; and, an imageenhancement component configured to receive the captured image and toutilize a point spread function (PSF) calculated from the calibrationdata to produce an enhanced image.
 2. The system of claim 1, wherein thelens is located on a camera and the image enhancement component and thecalibration data are located on a computing device that is configured toreceive the captured image from the camera and to reduce lens inducedaberrations on the captured image to produce the enhanced image.
 3. Thesystem of claim 1, wherein the lens, the calibration data, and the imageenhancement component are located on a camera.
 4. The system of claim 3,wherein the camera comprises a smart phone and wherein the imageenhancement component is a downloadable application on the smart phone.5. The system of claim 3, wherein the camera includes storage and aprocessor and wherein the image enhancement component is installed by amanufacturer of the camera on the storage for execution by theprocessor.
 6. The system of claim 3, wherein the lens is removablyconnected to the camera to allow interchanging with lenses of othermodels and wherein the image enhancement component is configured tostore other PSFs for other lenses.
 7. The system of claim 1, wherein thePSF is a lens specific PSF that is calculated from another PSF for amodel of the lens and that is fitted to the lens via the calibrationdata that is specific to the lens.
 8. The system of claim 1, wherein thecalibration data comprises a test image of the planar calibrationpattern captured through the lens at the single depth of field or thecalibration data comprises a measured PSF calculated from the testimage.
 9. A method, comprising: obtaining a point spread function (PSF)fitted to an individual lens; and, enhancing an image captured with theindividual lens using the fitted PSF.
 10. The method of claim 9, whereinthe obtaining a fitted PSF comprises: obtaining a test image of a planarcalibration pattern at a single depth of field; calculating a PSFmeasurement at the single depth of field; obtaining a simulated PSF of alens model for the lens, wherein the simulated PSF addresses aberrationsassociated with the lens model; and, fitting the simulated PSF with thePSF measurement for the individual lens to compensate for otheraberrations associated with manufacturing variances of the lens togenerate the fitted PSF for the lens.
 11. The method of claim 10,wherein the image is captured at a different depth of field and whereinthe enhancing comprises reducing the model induced aberrations and themanufacturing induced other aberrations at the different depth of field.12. The method of claim 10, wherein the obtaining a test image comprisesilluminating the planar calibration pattern with multiple differentlasers.
 13. The method of claim 10, wherein the obtaining a test imagecomprises capturing the test image.
 14. The method of claim 10, whereinthe individual lens is associated with a camera and the obtaining a testimage comprises obtaining the test image from the camera.
 15. The methodof claim 10, wherein the fitted PSF compensates for specific lensparameter values including different depths of field.
 16. The method ofclaim 9, stored as instructions on one or more computer-readable storagemedia that when executed by a processor of a computing device, theinstructions cause the computing device to perform the method.
 17. Adevice, comprising: a lens configured to focus images on an imagesensor; and, PSF lens data relating to manufacturing specifications ofthe lens and a PSF measurement of the lens associated with a test imageof a planar calibration pattern at a single depth of field.
 18. Thedevice of claim 17, wherein the PSF lens data comprises the test imageand the manufacturing specifications.
 19. The device of claim 17,wherein the PSF lens data comprises a fitted PSF that is based upon aPSF for the manufacturing specifications fitted to the PSF measurementof the test image.
 20. The device of claim 17, wherein the deviceincludes the image sensor.